Skip to main content
SpringerLink
Log in

Advances in Urban Video-Based Surveillance Systems: A Survey

  • Conference paper
  • First Online:
Soft Computing Applications (SOFA 2014)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 356))

Included in the following conference series:

Abstract

The focus of this paper is on providing the perspective intelligent technologies and systems for video-based urban surveillance. The development of intelligent transportation systems improves the safety on the road networks. Car manufacturers, public transportation services, and social institutions are interested in detecting pedestrians in the surroundings of a vehicle to avoid the dangerous traffic situations. Also the study of driver’s behavior has become a topic of interest in intelligent transportation systems. Another challenge deals with the intelligent vision technologies for pedestrians’ detection and tracking, which are fundamentally different from the crowd surveillance in public places during social events, sport competitions, etc. The detection of abnormal behavior is also connected with the human safety tasks. Some perspective methods of natural disaster surveillance such as earthquakes, fire, explosions, and terrorist attacks are briefly discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lee U, Magistretti E, Gerla M, Bellavista P, Corradi A (2009) Dissemination and harvesting of urban data using vehicular sensor platforms. IEEE Trans Vehiclar Technol 58(2):882–901

    Article  Google Scholar 

  2. Favorskaya M, Pyankov D, Popov A (2013) Motion estimations based on invariant moments for frames interpolation in stereovision. Procedia Comput Sci 22:1102–1111

    Article  Google Scholar 

  3. Zhao Y, Gong H, Jia Y, Zhu SC (2012) Background modeling by subspace learning on spatio-temporal patches. Pattern Recogn Lett 33(9):1134–1147

    Article  Google Scholar 

  4. Choi JM, Chang HJ, Yoo YJ, Choi JY (2012) Robust moving object detection against fast illumination change. Comput Vis Image Underst 116(2):179–193

    Article  Google Scholar 

  5. Lai AN, Yoon H, Lee G (2008) Robust background extraction scheme using histogram-wise for real-time tracking in urban traffic video. In: Proceedings of 8th IEEE international conference on computer and information technology (CIT 2008), Sydney, Australia, pp 845–850

    Google Scholar 

  6. Pilet J, Strecha C, Fua P (2008) Making background subtraction robust to sudden illumination changes. In: Proceedings of 10th European conference on computer vision (ECCV 2008), Marseille, France, pp 567–580

    Google Scholar 

  7. Favorskaya M, Pakhirka A (2012) A way for color image enhancement under complex luminance conditions. In: Watanabe T, Watada J, Takahashi N, Howlett RJ, Jain LC (eds) Smart innovation, systems and technologies, vol 14. pp 63–72

    Google Scholar 

  8. Pádua FLC, Carceroni R, Santos G, Kutulakos K (2010) Linear sequence-to-sequence alignment. IEEE Trans Pattern Anal Mach Intell 32(2):304–320

    Article  Google Scholar 

  9. Caspi Y, Simakov D, Irani M (2006) Feature-based sequence-to-sequence matching. Int J Comput Vision 68(1):53–64

    Article  Google Scholar 

  10. Liu Y, Yang M, You Z (2012) Video synchronization based on events alignment. Pattern Recogn Lett 33(10):1338–1348

    Article  Google Scholar 

  11. Wang X (2013) Intelligent multi-camera video surveillance: a review. Pattern Recogn Lett 34(1):3–19

    Article  Google Scholar 

  12. Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4): article no. 13

    Google Scholar 

  13. Cheng ED, Piccardi M (2006) Matching of objects moving across disjoint cameras. In: Proceedings of IEEE international conference on image processing (IPC 2006), Atlanta, GA, USA, pp 1769–1772

    Google Scholar 

  14. Schwartz W, Davis L (2009) Learning discriminative appearance-based models using partial least squares. In: Proceedings of XXII Brazilian symposium on computer graphics and image processing (SIBGRAPI), Rio de Janiero, Brazil, pp 322–329

    Google Scholar 

  15. Bay H, Tuytelaars T, Gool LV (2006) Surf: speed up robust features. Comput Vis Image Underst 110(3):346–359

    Article  Google Scholar 

  16. Forssen PE (2007) Maximally stable colour regions for recognition and matching. In: Proceedings of IEEE international conference on computer vision and pattern recognition (CVPR 2007), Minneapolis MN, USA, pp 1–8

    Google Scholar 

  17. Guo Y, Shan Y, Sawhney H, Kumar R (2007) Peet: prototype embedding and embedding transition for matching vehicles over disparate viewpoints. In: Proceedings of IEEE international conference on computer vision and pattern recognition (CVPR 2007), Minneapolis, MN, USA, pp 1–8

    Google Scholar 

  18. Wang X, Tieu K, Grimson E (2010) Correspondence-free activity analysis and scene modeling in multiple camera views. IEEE Trans Pattern Anal Mach Intell 32(1):56–71

    Article  Google Scholar 

  19. Liu M, Wu C, Zhang Y (2008) A review of traffic visual tracking technology. In: Proceedings of international conference on audio, language and image processing (ICALIP 2008), Shanghai, pp 1016–1020

    Google Scholar 

  20. Cao X, Shi Z, Yan P, Li X (2013) Tracking vehicles as groups in airborne videos. Neurocomputing 99(1):38–45

    Article  Google Scholar 

  21. Chien JC, Lee JD, Chen CM, Fan MW, Chen YH, Liu LC (2013) An integrated driver warning system for driver and pedestrian safety. Appl Soft Comput 13(11):4413–4427

    Article  Google Scholar 

  22. Trivedi MM, Gandhi T, McCall J (2007) Looking-in and looking-out of a vehicle: computer-vision-based enhanced vehicle safety. IEEE Trans Intell Transp Syst 8(1):108–120

    Article  Google Scholar 

  23. Klauer SG, Dingus TA, Neale VL, Sudweeks JD, Ramsey DJ (2006) The impact of driver inattention on near-crash/crash risk: an analysis using the 100-car naturalistic driving study data. In: Technical report DOT HS 810 594 of the Virginia tech transportation institute, NHTSA

    Google Scholar 

  24. Kircher K (2007) Driver distraction: a review of the literature. In: Technical report 594A of the Swedish national road and transport research institute

    Google Scholar 

  25. Cheng SY, Park S, Trivedi MM (2007) Multi-spectral and multi-perspective video arrays for driver body tracking and activity analysis. Comput Vis Image Underst 106(2–3):245–257

    Article  Google Scholar 

  26. Wada T, Yoshida M, Doi S, Tsutsumi S (2010) Characterization of hurried driving based on collision risk and attentional allocation. In: Proceedings of 13th international IEEE conference on intelligent transportation systems (ITSC’10), Funchal, Portugal, pp 623–628

    Google Scholar 

  27. Wang J, Zhu S, Gong Y (2010) Driving safety monitoring using semi-supervised learning on time series data. IEEE Trans Intell Transp Syst 11(3):728–737

    Article  Google Scholar 

  28. Baro X, Escalera S, Vitria J, Pujol O, Radeva P (2009) Traffic sign recognition using evolutionary adaboost detection and forest-ecoc classification. IEEE Trans Intell Transp Syst 10(1):113–126

    Article  Google Scholar 

  29. Stallkamp J, Schlipsing M, Salmen J, Igel C (2012) Man vs. computer: benchmarking machine learning algorithms for traffic sign recognition. Neural Netw 32:323–332

    Google Scholar 

  30. Chang CC, Hsieh YP (2012) A fast VQ codebook search with initialization and search order. Inform Sci 183(1):132–139

    Article  Google Scholar 

  31. Gavrila D (2000) Pedestrian detection from a moving vehicle. In: Proceedings of 6th European conference on computer vision (ECCV 2000), Dublin, Ireland, Part II, pp 2241–2248

    Google Scholar 

  32. Szarvas M, Yoshizawa A, Yamamoto M, Ogata J (2005) Pedestrian detection with convolutional neural networks. In: Proceedings of IEEE intelligent vehicles symposium, Nevada, USA, pp 224–229

    Google Scholar 

  33. Zhang G, Gao F, Liu C, Liu W, Yuan H (2010) A pedestrian detection method based on SVM classifier and optimized histograms of oriented gradients feature. In: Proceedings of 6th international conference on natural computation (ICNC 2010), Yantai, Shandong, vol 6. pp 3257–3260

    Google Scholar 

  34. Malagon-Borja L, Fuentes O (2007) Object detection using image reconstruction with PCA. Image Vis Comput 27(1–2):2–9

    Google Scholar 

  35. Geronimo D, Sappa AD, Lopez A, Ponsa D (2006) Pedestrian detection using adaboost learning of features and vehicle pitch estimation. In: Proceedings of 6th IASTED international conference on visualization (VIIP 2006), Palma De Mallorca, Spain, pp 400–405

    Google Scholar 

  36. Jones M, Viola P (2003) Detecting pedestrians using patterns of motion and appearance. In: Proceedings of IEEE international conference on computer vision (ICCV 2003), Nice, France, vol 2. pp 734–741

    Google Scholar 

  37. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition (CVPR 2005), San Diego, CA, USA, vol 1. pp 886–893

    Google Scholar 

  38. Hota VVRN, Rajagopal A (2007) Shape based object classification for automated video surveillance with feature selection. In: Proceedings of 10th international conference on information technology (ICIT 2007), Orissa, India, pp 97–99

    Google Scholar 

  39. Nguyen THB, Kim H (2013) Novel and efficient pedestrian detection using bidirectional PCA. Pattern Recogn 46(8):2220–2227

    Article  Google Scholar 

  40. Favorskaya M (2012) Motion estimation for object analysis and detection in videos. In: Kountchev R, Nakamatsu K (eds) Advances in reasoning-based image processing, analysis and intelligent systems, vol 29. Springer, Berlin, Heidelberg, pp 211–253

    Google Scholar 

  41. Lucas B, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: Proceedings of 7th international joint conference on artificial intelligence (IJCAI’81), San Francisco, USA, vol 2. pp 674–679

    Google Scholar 

  42. Laptev I, Marszalek M, Schmid C, Rozenfeld B (2008) Learning realistic human actions from movies. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR 2008), Anchorage, AK, USA, pp 1–8

    Google Scholar 

  43. Efros A, Berg A, Mori G, Malik J (2003) Recognizing action at a distance. In: Proceedings of 9th IEEE international conference on computer vision (ICCV 2003), Washington, DC, USA, vol 2. pp 726–733

    Google Scholar 

  44. Zelnik-Manor L, Irani M (2001) Event-based analysis of video. In: Proceeding of the 2001 IEEE computer society conference on computer vision and pattern recognition (CVPR 2001), Kauai, HI, USA, vol 2. pp 123–130

    Google Scholar 

  45. Park S, Aggarwal JK (2006) Simultaneous tracking of multiple body parts of interacting persons. Comput Vis Image Underst 102(1):1–21

    Article  Google Scholar 

  46. Mikolajczyk K, Uemura H (2011) Action recognition with appearance–motion features and fast search trees. Comput Vis Image Underst 115(3):426–438

    Article  Google Scholar 

  47. Silveira Jacques JJCS, Musse RS, Jung RC (2010) Crowd analysis using computer vision techniques. IEEE Signal Proc Mag 27(5):66–77

    Google Scholar 

  48. Zhan B, Monekosso D, Remagnino P, Velastin S, Xu LQ (2008) Crowd analysis: a survey. Mach Vis Appl 19(5–6):345–357

    Article  MATH  Google Scholar 

  49. Gerónimo D, López A, Sappa AD (2007) Computer vision approaches to pedestrian detection: visible spectrum survey. Pattern Recogn Image Anal 4477:547–554

    Article  Google Scholar 

  50. Dong L, Parameswaran V, Ramesh V, Zoghlami I (2007) Fast crowd segmentation using shape indexing. In: Proceedings of 11th international conference on computer vision (ICCV’2007), Rio de Janeiro, Brazil, pp 1–8

    Google Scholar 

  51. Wang L, Yung NHC (2009) Crowd counting and segmentation in visual surveillance. In: Proceedings of 16th IEEE international conference on image processing (ICIP’2009), Cairo, Egypt, pp 2573–2576

    Google Scholar 

  52. Ali S, Shah MA (2007) A lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In: Proceedings of IEEE international conference on computer vision and pattern recognition (CVPR 2007), Minneapolis, MN, pp 1–6

    Google Scholar 

  53. Fagette A, Courty N, Racoceanu D, Dufour JY (2014) Unsupervised dense crowd detection by multiscale texture analysis. Pattern Recogn Lett (2014) (in print)

    Google Scholar 

  54. Ali I, Dailey MN (2012) Multiple human tracking in high-density crowds. Image Vis Comput 30(12):966–977

    Article  Google Scholar 

  55. Sharif MdH, Djeraba C (2012) An entropy approach for abnormal activities detection in video streams. Pattern Recogn 45(7):2543–2561

    Article  Google Scholar 

  56. Ihaddadene N, Djeraba C (2008) Real-time crowd motion analysis. In: Proceedings of 19th international conference on pattern recognition (ICPR 2008), Tampa, FL, USA, pp 1–4

    Google Scholar 

  57. Ivanov I, Dufaux F, Ha TM, Ebrahimi T (2009) Towards generic detection of unusual events in video surveillance. In: Proceedings of international conference on advanced video and signal based surveillance (AVSS’09), Genova, Italy, pp 61–66

    Google Scholar 

  58. Xiang T, Gong S (2008) Video behavior profiling for anomaly detection. IEEE Trans Pattern Anal Mach Intell 30(5):893–908

    Article  Google Scholar 

  59. Mehran R, Oyama A, Shah M (2009) Abnormal crowd behavior detection using social force model. In: Proceedings of international conference computer vision and pattern recognition (CVPR 2009), Miami, FL, USA, pp 935–942

    Google Scholar 

  60. Hu W, Xiao X, Fu Z, Xie D, Tan T, Maybank SJ (2006) A system for learning statistical motion patterns. IEEE Trans Pattern Anal Mach Intell 28(9):1450–1464

    Article  Google Scholar 

  61. Stauffer C, Grimson WL (2000) Learning patterns of activity using real-time tracking. IEEE Trans Pattern Anal Mach Intell 22(8):747–757

    Article  Google Scholar 

  62. Smeelen MA, Schwering PBW, Toet A, Loog M (2014) Semi-hidden target recognition in gated viewer images fused with thermal IR images. Inform Fusion 18:131–147

    Article  Google Scholar 

  63. Zaveri T, Zaveri MA (2011) A novel region based multimodality image fusion method. J Pattern Recogn Res 6(2):140–153

    Article  Google Scholar 

  64. Krishnamoorthy S, Soman KP (2010) Implementation and comparative study of image fusion algorithms. Int J Comput Appl 9(2):25–35

    Google Scholar 

  65. De Filippis L, Guglieri G (2012) Path Planning strategies for UAVs in 3D environments. J Intell Rob Syst 65(1):247–264

    Article  Google Scholar 

  66. Grocholsky B, Keller J, Kumar RV, Pappas GJ (2006) Cooperative air and ground surveillance. IEEE Robot Autom Mag 13(3):16–25

    Article  Google Scholar 

  67. Li Q, Li DC, Wua QF, Tang LW, Huo Y, Zhang YX, Cheng NL (2013) Autonomous navigation and environment modeling for MAVs in 3-D enclosed industrial environments. Comput Ind 64(9):1161–1177

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Informatics and Telecommunication, Siberian State Aerospace University, 31, Krasnoyarsky Rabochy Av., 660014, Krasnoyarsk, Russia

    M. Favorskaya

Authors
  1. M. Favorskaya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Favorskaya .

Editor information

Editors and Affiliations

  1. Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  2. Faculty of Science and Technology, Data Science Institute, Bournemouth University, Poole, United Kingdom

    Lakhmi C. Jain

  3. University of Belgrade, Belgrade, Serbia

    Branko Kovačević

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Favorskaya, M. (2016). Advances in Urban Video-Based Surveillance Systems: A Survey. In: Balas, V., C. Jain, L., Kovačević, B. (eds) Soft Computing Applications. SOFA 2014. Advances in Intelligent Systems and Computing, vol 356. Springer, Cham. https://doi.org/10.1007/978-3-319-18296-4_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-18296-4_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18295-7

  • Online ISBN: 978-3-319-18296-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation